There have been suggested various techniques for generating a high-resolution image having a higher resolution from a plurality of low-resolution images obtained by taking pictures of the same subject (for example, see Patent Literature (PTL) 1). This high-resolution image is referred to as a super-resolution image.
In recent years, there has been increased a need for a video super-resolution technique for generating a dynamic image having a higher resolution from a low-resolution dynamic image by applying the technique not only to static images, but also to a dynamic image. The video super-resolution technique is used for upconversion, for example, for use in displaying a video picture, which has been taken at standard-definition (SD) resolution, on a high-definition (HD) television receiver.
PTL 1 describes a high-resolution image generation method for generating a high-resolution image from a plurality of low-resolution images with position deviation. In the method described in PTL 1, each pixel of the plurality of low-resolution images is associated with a certain position in a high-resolution image space. More specifically, after registration, the plurality of low-resolution images are assumed to be pixels sampled in an unequally spaced manner within the high-resolution space. In the method described in PTL 1, the locations of the pixels sampled in the unequally spaced manner (referred to as observed pixel locations) are approximated to the pixel locations of the high-resolution image. In this instance, the observed pixel locations, which are approximated to the high-resolution pixel locations, may be a plurality of pixel locations or no pixel locations in some cases. In the method described in PTL 1, one image is generated by calculating a mean value of a plurality of observed pixels approximated to the respective high-resolution pixel locations. In the method described in PTL 1, this image is referred to as “mean image.” Similarly, the observed pixels approximated to the respective high-resolution pixel locations also constitute one image. In PTL 1, this image is referred to as “weighted image.” In the method described in PTL 1, the mean image and the weighted image are generated from the plurality of low-resolution images and the position deviation information of the low-resolution images obtained by registration. Thereafter, a high-resolution image is generated by estimating the pixel value of an undefined pixel included in the generated mean image. Moreover, in the method described in PTL 1, every time a low-resolution image is obtained, the mean image and the weighted image are updated to generate a high-resolution image sequentially.
Furthermore, as a technique for estimating the states of various systems or the like, a Kalman filter is known. The Kalman filter is described in, for example, Non Patent Literature (NPL) 1. In the Kalman filter described in NPL 1, a least squares solution is calculated by sequentially repeating the prediction and update of the states in the case where noises wt and vt conform to the normal distribution given by expression (1) and expression (2) described below.wt to N(0,Q)  Expression (1)vt to N(0,R)  Expression (2)